POS1179 USING ARTIFICIAL INTELLIGENCE TO IDENTIFY ANTI-NEUTROPHIL CYTOPLASMATIC ANTIBODY (ANCA)-ASSOCIATED VASCULITIS PATIENTS IN ELECTRONIC HEALTH RECORDS
Authors: J. Van Leeuwen, E. Penne, Y. K. O. Teng
Journal: Annals of the Rheumatic Diseases (2023)

<h3>Background</h3> Anti-neutrophil cytoplasmatic antibody (ANCA) associated vasculitis (AAV) is a rare, life-threatening, systemic auto-immune disease.<sup>[1]</sup> Due to the low prevalence, multiple treating disciplines and poor registration, including ICD-10 classification, identifying AAV patients for (pre-)clinical studies, research and health care evaluation is challenging.<sup>[2, 3]</sup> Therefore, there is an urgent need to improve identifying these patients in health care organisations. Employing artificial intelligence (AI) – supported search engines are increasingly suggested to achieve this. <h3>Objectives</h3> Reliably identify AAV patients in electronic health records (EHR) using an AI-based tool that incorporates text mining and Natural Language Processing (NLP). <h3>Methods</h3> The identification method consists of a search strategy combined with NLP-based exclusion. A search strategy to optimally identify AAV patients within a single center EHR system of an academic hospital was developed using an established AAV cohort (n=203) as a gold-standard reference set. Patient records, identified by the search strategy outside of the reference set, underwent manual review to confirm newly-identified AAV patients. Then, improvement in performance by adding NLP to the text-mining search strategy was investigated. Validation was performed on an independent EHR system of a non-academic hospital with an established AAV cohort available for reference (n=84). <h3>Results</h3> The search strategy combined five queries based on disease description, laboratory measurements, medication and relevant specialisms. In the determination EHR, the search strategy identified 608 patients, including 197/203 (97.0%) AAV patients of the reference set and 149 newly-identified AAV cases confirmed by manual review. Employing NLP in the identification method improved the positive predictive value (PPV) from 57% (346/608 patients) to 78% (339/444 patients). These results were validated in an independent EHR system where the search strategy identified 333 patient, including 82/84 (97.6%) AAV patients of the reference set and 112 newly-identified AAV cases upon manual review. NLP improved PPV from 59% (194/333 patients) to 86% (192/223 patients). Negative predictive values and specificities were above 98% in all analyses. <h3>Conclusion</h3> We present an AI based identification method to identify low-prevalent AAV patients in EHR systems. We demonstrated improved performance when adding NLP to the text-mining search strategy. Successful validation in an independent health organisation supports the applicability and transportability of this method which can be an important accelerator for research efforts and health care evaluation in AAV patients. <h3>References</h3> [1]Kitching AR, Anders HJ, Basu N, Brouwer E, Gordon J, Jayne DR, et al. ANCA-associated vasculitis. Nat Rev Dis Primers. 2020;6(1):71. [2]Quan H, Li B, Saunders LD, Parsons GA, Nilsson CI, Alibhai A, et al. Assessing validity of ICD-9-CM and ICD-10 administrative data in recording clinical conditions in a unique dually coded database. Health Serv Res. 2008;43(4):1424-41. [3]Spierings J BT, Dirikgil E, Moens HB. Overdaad aan ICD-coderingen hindert onderzoek. Medisch Contact. 2020;22:18-20. <h3>Acknowledgements:</h3> NIL. <h3>Disclosure of Interests</h3> Jolijn van Leeuwen: None declared, Erik Penne: None declared, Y.K. Onno Teng Consultant of: YKOT received an unrestricted research grant and consultancy fees from Vifor Pharma., Grant/research support from: The work of YKOT was supported by the Dutch Kidney Foundation (17OKG04) and by the Arthritis Research and Collaboration Hub Foundation. Arthritis Research and Collaboration Hub is funded by Dutch Arthritis Foundation (ReumaNederland).